8 May 2001 Nonlinear filtering and pattern recognition: Are they the same?
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Statistical design of window-based nonlinear filters for signal and image processing is closely related to pattern recognition. The theory of pattern recognition is concerned with estimating the errors of optimal classifiers and with designing classifiers from sample data whose errors are close to minimal. Of special importance is the Vapnik- Chervonenkis theory, which relates the design cost to the VC dimension of a classification rule. This paper discusses both constraint and design costs for the design of nonlinear filters, and discusses the relation to the theory of pattern recognition. As to the question posed in the title, the paper argues that nonlinear filtering possesses its own integrity because classification rules and constraints depend on signal and image properties, both in theory and the manner in which expert knowledge is applied in design.
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Edward R. Dougherty, Edward R. Dougherty, Junior Barrera, Junior Barrera, } "Nonlinear filtering and pattern recognition: Are they the same?", Proc. SPIE 4304, Nonlinear Image Processing and Pattern Analysis XII, (8 May 2001); doi: 10.1117/12.424961; https://doi.org/10.1117/12.424961

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